Project-oriented methodology for proactive epidemic management based on artificial intelligence and SEIRS models
DOI:
https://doi.org/10.30837/2522-9818.2026.2.095Keywords:
proactive epidemic management; project-oriented methodology; artificial intelligence; SEIRS model; machine learning; digital health; epidemiological forecasting; decision support systemsAbstract
The subject of the research presented in the article is the processes of proactive epidemic management as complex socio-medical systems under conditions of high uncertainty, limited resources, and dynamic external influences, including the use of mathematical epidemiological modeling and intelligent digital technologies. The purpose of the study is to develop a project-oriented methodology for epidemic management based on the integration of the classical SEIRS model with artificial intelligence, machine learning, and digital decision-support systems, enabling a transition from reactive response to proactive forecasting and prevention of infectious disease outbreaks. The following tasks are addressed in the article: the formation of a conceptual and process-based model of proactive epidemic management within a project-oriented approach; the development of an integrated architecture for the collection, analysis, and interpretation of epidemiological data; the adaptation of the SEIRS compartmental model to dynamic parameterization using machine learning methods; the justification of geospatial and multi-agent approaches for capturing heterogeneity in disease spread; and the identification of the role of intelligent optimization methods in managing interventions throughout the epidemic project lifecycle. The following methods are used: mathematical modeling of epidemiological processes based on systems of SEIRS differential equations; machine learning and ensemble forecasting methods for dynamic estimation of model parameters; geospatial analysis and multi-agent modeling to represent population mobility and spatial risk patterns; reinforcement learning methods for optimizing management decisions; and project-oriented management approaches for organizing and adapting epidemic response processes. The following results are obtained: principles of proactive epidemic management based on the integration of epidemiological modeling and artificial intelligence are formulated; a project-oriented methodology that aligns the project management cycle with the phases of the SEIRS model is proposed; a generalized structure of a hybrid intelligent model capable of adapting to different types of infectious diseases, including tuberculosis and COVID-19, is developed; and the potential for improving early risk detection and rational resource allocation under crisis conditions is substantiated. Conclusions: the application of the proposed project-oriented methodology and the integrated SEIRS model enhanced with artificial intelligence provides a foundation for the transition to proactive epidemic management, increases the adaptability of public health systems, and can be used as a universal methodological framework for the development of intelligent epidemic forecasting and prevention systems in complex and unstable environments.Downloads
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